Plan your next seasonal promotion from last season’s evidence

When demand for your category peaks, what three competitors offered last season on their archived pages and in their ads, and a promo calendar for this year.

Steps

At most, per call

A seasonal promo plan times your offer to demand and positions it against what competitors did last year. With Agentik, your AI reads five years of Google Trends with DataForSEO, finds and reads competitors’ pages from last season with the Wayback Machine and Firecrawl, and reads their promotion ads with Adspy.sh.

Step 1: Time the demand
Google Trends over five years shows when demand for your category rises and peaks.
- Google Trends interest over time and by regionDataForSEO · 5 terms$0.04
Step 2: Read last season’s offers
The Wayback Machine finds competitors’ home and promotion pages from last season, and Firecrawl reads them.
- Wayback Machine snapshot lookupInternet Archive · 6 pagesFree
- Scrape a web page to markdownFirecrawl · 6 archived pages$0.02
Step 3: Read their promotion ads
Adspy.sh finds the promotion ads your three competitors ran.
- Search ad creatives on Meta, Google and TikTokAdspy.sh · 30 ads$0.06
Step 4: Build the calendar
Your AI writes the calendar with dates, offers, channels and creative angles.
Your agent does this part, with no tool call.

Cost of the example

At most $0.11one run

Five years of Google Trends for 5 terms, 6 archived pages found and read, Black Friday ads from 3 competitors.

Give this to your agent

Use my connected Agentik MCP server for this job: seasonal promo plan.

Plan my next seasonal promotion. First ask me for my category, the season (for example Black Friday), my country and three competitors.

Read five years of demand with dataforseo/google-trends. Find competitors’ home and promo pages from last season with wayback/availability and read them with firecrawl/scrape. Find their promotion ads with adspy/search-creatives.

Finish with the demand curve and peak dates, what each competitor offered and when, and a calendar for my promotion (dates, offer, channels, creative angles).

Before running anything, inspect each tool with inspect_tool, ask me for any input you don’t have instead of inventing it, and tell me the total price. Wait for my go. At the end, tell me what Agentik charged.

4 providers, one connection

What you get

A dated promotion calendar.

You provide

Your category
The season
Country
Three competitors

Your agent returns

The demand curve and peak dates
What competitors offered last season, and when
Their promotion ads
A dated promo calendar for you

Promo calendarExample layout

Date	Offer	Channel	Angle
Two weeks before	Early access	Email	Members first
Peak week	Bundle discount	Meta and Google	Gift ready
After the peak	Free shipping	Email	Last chance

Placeholders. Your rows come from your run.

Each provider does one part

No single provider covers this job. Your agent calls each one for its part, from one connection.

Good to know

The archive keeps pages it happened to crawl; some days may be missing.
Ad dates show when Adspy observed an ad, not its whole schedule.

When to use something else

For promotion management and discount codes in your store, use your e-commerce platform. This playbook decides when and what to offer, from last season’s evidence.

Questions

What if the archive missed a page?

The Wayback Machine keeps the pages it crawled; your agent takes the nearest copy and says so.

Does it work for any season?

Yes: Black Friday, back to school, summer sales or a local holiday. Your agent asks for the dates.

What does the example cost?

At most $0.11, reserved before each call and settled at the measured usage.

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